本發明實施例提供一種資料監控方法、裝置、電子設備及電腦可讀儲存媒體。
第一態樣，本發明實施例中提供了一種資料監控方法。
具體的，所述資料監控方法，包括：
回應於接收到資料，確定所述資料是否屬於當前監控資料區間，其中，所述資料的接收頻率高於預設頻率臨限值；
當所述資料不屬於所述當前監控資料區間時，確定是否存在與所述資料匹配的異常資料區間，當存在與所述資料匹配的異常資料區間時，根據所述資料對所述異常資料區間進行更新；
當所述異常資料區間滿足第一預設條件時，將所述異常資料區間確定為當前監控資料區間。
結合第一態樣，本發明實施例在第一態樣的第一種實現方式中，所述回應於接收到資料，確定所述資料是否屬於當前監控資料區間，包括：
回應於接收到資料，確定是否存在當前監控資料區間；
當存在當前監控資料區間時，確定所述資料是否屬於所述當前監控資料區間；
當不存在當前監控資料區間時，根據接收到的資料創建當前監控資料區間。
結合第一態樣和第一態樣的第一種實現方式，本發明實施例在第一態樣的第二種實現方式中，所述回應於接收到資料，確定所述資料是否屬於當前監控資料區間之後，包括：
當所述資料屬於所述當前監控資料區間時，根據所述資料對所述當前監控資料區間進行更新。
結合第一態樣、第一態樣的第一種實現方式和第一態樣的第二種實現方式，本發明實施例在第一態樣的第三種實現方式中，所述當所述資料屬於所述當前監控資料區間時，根據所述資料對所述當前監控資料區間進行更新的步驟，包括：
當所述資料屬於所述當前監控資料區間時，根據所述資料對所述當前監控資料區間進行更新；
刪除異常資料區間。
結合第一態樣、第一態樣的第一種實現方式、第一態樣的第二種實現方式和第一態樣的第三種實現方式，本發明實施例在第一態樣的第四種實現方式中，所述當所述資料不屬於所述當前監控資料區間時，確定是否存在與所述資料匹配的異常資料區間，當存在與所述資料匹配的異常資料區間時，根據所述資料對所述異常資料區間進行更新，包括：
當所述資料不屬於所述當前監控資料區間時，確定是否存在與所述資料匹配的異常資料區間；
當存在與所述資料匹配的異常資料區間時，根據所述資料對所述異常資料區間進行更新；
當不存在與所述資料匹配的異常資料區間時，新建異常資料區間。
結合第一態樣、第一態樣的第一種實現方式、第一態樣的第二種實現方式、第一態樣的第三種實現方式和第一態樣的第四種實現方式，本發明實施例在第一態樣的第五種實現方式中，所述當不存在與所述資料匹配的異常資料區間時，新建異常資料區間，包括：
當不存在與所述資料匹配的異常資料區間時，確定所述異常資料區間的數量是否大於預設數量臨限值；
當所述異常資料區間的數量大於所述預設數量臨限值時，刪除滿足第二預設條件的異常資料區間，新建異常資料區間；
當所述異常資料區間的數量不大於所述預設數量臨限值時，新建異常資料區間。
結合第一態樣、第一態樣的第一種實現方式、第一態樣的第二種實現方式、第一態樣的第三種實現方式、第一態樣的第四種實現方式和第一態樣的第五種實現方式，本發明實施例在第一態樣的第六種實現方式中，所述當所述異常資料區間滿足第一預設條件時，將所述異常資料區間確定為當前監控資料區間，包括：
當所述異常資料區間滿足第一預設條件時，將所述異常資料區間確定為當前監控資料區間；
刪除異常資料區間。
結合第一態樣、第一態樣的第一種實現方式、第一態樣的第二種實現方式、第一態樣的第三種實現方式、第一態樣的第四種實現方式、第一態樣的第五種實現方式和第一態樣的第六種實現方式，本發明實施例在第一態樣的第七種實現方式中，根據資料對當前監控資料區間或異常資料區間進行更新，包括以下更新操作中的一種或幾種：
將所述當前監控資料區間或異常資料區間的區間中心值更新為所述資料，並根據預設區間長度更新所述當前監控資料區間或異常資料區間的區間範圍；
更新所述當前監控資料區間或異常資料區間中的資料數量；
更新所述當前監控資料區間或異常資料區間的資料更新時間。
第二態樣，本發明實施例中提供了一種資料監控裝置。
具體的，所述資料監控裝置，包括：
第一確定模組，被配置為回應於接收到資料，確定所述資料是否屬於當前監控資料區間，其中，所述資料的接收頻率高於預設頻率臨限值；
第二確定模組，被配置為當所述資料不屬於所述當前監控資料區間時，確定是否存在與所述資料匹配的異常資料區間，當存在與所述資料匹配的異常資料區間時，根據所述資料對所述異常資料區間進行更新；
第三確定模組，被配置為當所述異常資料區間滿足第一預設條件時，將所述異常資料區間確定為當前監控資料區間。
結合第二態樣，本發明實施例在第二態樣的第一種實現方式中，所述第一確定模組包括：
第一確定子模組，被配置為回應於接收到資料，確定是否存在當前監控資料區間；
第二確定子模組，被配置為當存在當前監控資料區間時，確定所述資料是否屬於所述當前監控資料區間；
創建子模組，被配置為當不存在當前監控資料區間時，根據接收到的資料創建當前監控資料區間。
結合第二態樣和第二態樣的第一種實現方式，本發明實施例在第二態樣的第二種實現方式中，所述裝置還包括：
更新模組，被配置為當所述資料屬於所述當前監控資料區間時，根據所述資料對所述當前監控資料區間進行更新。
結合第二態樣、第二態樣的第一種實現方式和第二態樣的第二種實現方式，本發明實施例在第二態樣的第三種實現方式中，所述更新模組包括：
第一更新子模組，被配置為當所述資料屬於所述當前監控資料區間時，根據所述資料對所述當前監控資料區間進行更新；
第一刪除子模組，被配置為刪除異常資料區間。
結合第二態樣、第二態樣的第一種實現方式、第二態樣的第二種實現方式和第二態樣的第三種實現方式，本發明實施例在第二態樣的第四種實現方式中，所述第二確定模組包括：
第三確定子模組，被配置為當所述資料不屬於所述當前監控資料區間時，確定是否存在與所述資料匹配的異常資料區間；
第二更新子模組，被配置為當存在與所述資料匹配的異常資料區間時，根據所述資料對所述異常資料區間進行更新；
第一新建子模組，被配置為當不存在與所述資料匹配的異常資料區間時，新建異常資料區間。
結合第二態樣、第二態樣的第一種實現方式、第二態樣的第二種實現方式、第二態樣的第三種實現方式和第二態樣的第四種實現方式，本發明實施例在第二態樣的第五種實現方式中，所述第一新建子模組包括：
第四確定子模組，被配置為當不存在與所述資料匹配的異常資料區間時，確定所述異常資料區間的數量是否大於預設數量臨限值；
第二新建子模組，被配置為當所述異常資料區間的數量大於所述預設數量臨限值時，刪除滿足第二預設條件的異常資料區間，新建異常資料區間；
第三新建子模組，被配置為當所述異常資料區間的數量不大於所述預設數量臨限值時，新建異常資料區間。
結合第二態樣、第二態樣的第一種實現方式、第二態樣的第二種實現方式、第二態樣的第三種實現方式、第二態樣的第四種實現方式和第二態樣的第五種實現方式，本發明實施例在第二態樣的第六種實現方式中，所述第三確定模組包括：
第五確定子模組，被配置為當所述異常資料區間滿足第一預設條件時，將所述異常資料區間確定為當前監控資料區間；
第二刪除子模組，被配置為刪除異常資料區間。
結合第二態樣、第二態樣的第一種實現方式、第二態樣的第二種實現方式、第二態樣的第三種實現方式、第二態樣的第四種實現方式、第二態樣的第五種實現方式和第二態樣的第六種實現方式，本發明實施例在第二態樣的第七種實現方式中，所述更新模組或更新子模組被配置為實現以下更新操作中的一種或幾種：
將所述當前監控資料區間或異常資料區間的區間中心值更新為所述資料，並根據預設區間長度更新所述當前監控資料區間或異常資料區間的區間範圍；
更新所述當前監控資料區間或異常資料區間中的資料數量；
更新所述當前監控資料區間或異常資料區間的資料更新時間。
第三態樣，本發明實施例提供了一種電子設備，包括記憶體和處理器，所述記憶體用於儲存一條或多條支持資料監控裝置執行上述第一態樣中資料監控方法的電腦指令，所述處理器被配置為用於執行所述記憶體中儲存的電腦指令。所述資料監控裝置還可以包括通信介面，用於資料監控裝置與其他設備或通信網路通信。
第四態樣，本發明實施例提供了一種電腦可讀儲存媒體，用於儲存資料監控裝置所用的電腦指令，其包含用於執行上述第一態樣中資料監控方法為資料監控裝置所涉及的電腦指令。
本發明實施例提供的技術方案可以包括以下有益效果：
上述技術方案通過設置多個資料區間，將新接收的資料與多個資料區間順序比對，並結合資料區間的特點來判斷新接收的資料是否屬於正常的資料波動。該技術方案能夠避免對於正常的、但幅度較大的資料波動出現誤判的情況，進而實現準確監控資料的目的。
應當理解的是，以上的一般描述和後文的細節描述僅是示例性和解釋性的，並不能限制本發明實施例。
Embodiments of the present invention provide a data monitoring method, device, electronic equipment, and computer-readable storage medium.
In the first aspect, an embodiment of the present invention provides a data monitoring method.
Specifically, the data monitoring method includes:
In response to receiving the data, determine whether the data belongs to the current monitoring data interval, where the frequency of receiving the data is higher than the preset frequency threshold;
When the data does not belong to the current monitoring data interval, it is determined whether there is an abnormal data interval matching the data, and when there is an abnormal data interval matching the data, the abnormal data interval is determined according to the data Update
When the abnormal data interval meets the first preset condition, the abnormal data interval is determined as the current monitoring data interval.
With reference to the first aspect, in a first implementation manner of the first aspect, the embodiment of the present invention, in response to receiving the data, determining whether the data belongs to the current monitoring data interval includes:
In response to receiving data, determine whether there is a current monitoring data interval;
When there is a current monitoring data interval, determine whether the data belongs to the current monitoring data interval;
When there is no current monitoring data interval, the current monitoring data interval is created based on the received data.
With reference to the first aspect and the first implementation manner of the first aspect, in a second implementation manner of the first aspect, the embodiment of the present invention, in response to receiving the data, determines whether the data belongs to current monitoring After the data interval, including:
When the data belongs to the current monitoring data interval, the current monitoring data interval is updated according to the data.
With reference to the first aspect, the first implementation manner of the first aspect, and the second implementation manner of the first aspect, in the third implementation manner of the first aspect, the embodiment of the present invention When the data belongs to the current monitoring data interval, the step of updating the current monitoring data interval according to the data includes:
When the data belongs to the current monitoring data interval, update the current monitoring data interval according to the data;
Delete the abnormal data interval.
Combining the first aspect, the first implementation manner of the first aspect, the second implementation manner of the first aspect, and the third implementation manner of the first aspect, the embodiment of the present invention is located in the first aspect of the first aspect. In the four implementations, when the data does not belong to the current monitoring data interval, it is determined whether there is an abnormal data interval matching the data, and when there is an abnormal data interval matching the data, according to The data update the abnormal data interval, including:
When the data does not belong to the current monitoring data interval, determine whether there is an abnormal data interval matching the data;
When there is an abnormal data interval matching the data, update the abnormal data interval according to the data;
When there is no abnormal data interval matching the data, a new abnormal data interval is created.
Combining the first aspect, the first implementation of the first aspect, the second implementation of the first aspect, the third implementation of the first aspect, and the fourth implementation of the first aspect, In a fifth implementation manner of the first aspect of the embodiment of the present invention, when there is no abnormal data interval matching the data, creating a new abnormal data interval includes:
When there is no abnormal data interval matching the data, it is determined whether the number of the abnormal data intervals is greater than the preset number threshold;
When the number of the abnormal data intervals is greater than the preset number threshold, delete the abnormal data intervals that satisfy the second preset condition, and create a new abnormal data interval;
When the number of the abnormal data intervals is not greater than the preset number threshold, a new abnormal data interval is created.
Combining the first aspect, the first implementation of the first aspect, the second implementation of the first aspect, the third implementation of the first aspect, and the fourth implementation of the first aspect and In a fifth implementation manner of the first aspect, in a sixth implementation manner of the first aspect of the embodiment of the present invention, when the abnormal data interval meets the first preset condition, the abnormal data interval Determined as the current monitoring data interval, including:
When the abnormal data interval meets the first preset condition, determine the abnormal data interval as the current monitoring data interval;
Delete the abnormal data interval.
Combining the first aspect, the first implementation of the first aspect, the second implementation of the first aspect, the third implementation of the first aspect, and the fourth implementation of the first aspect, In the fifth implementation manner of the first aspect and the sixth implementation manner of the first aspect, in the seventh implementation manner of the first aspect, the embodiment of the present invention compares the current monitoring data interval or the abnormal data interval according to the data Update, including one or more of the following update operations:
Updating the central value of the interval of the current monitoring data interval or the abnormal data interval to the data, and updating the interval range of the current monitoring data interval or the abnormal data interval according to the preset interval length;
Update the number of data in the current monitoring data interval or abnormal data interval;
Update the data update time of the current monitoring data interval or the abnormal data interval.
In a second aspect, an embodiment of the present invention provides a data monitoring device.
Specifically, the data monitoring device includes:
The first determining module is configured to determine whether the data belongs to the current monitoring data interval in response to receiving the data, wherein the frequency of receiving the data is higher than a preset frequency threshold;
The second determination module is configured to determine whether there is an abnormal data interval that matches the data when the data does not belong to the current monitoring data interval, and when there is an abnormal data interval that matches the data, according to The data updates the abnormal data interval;
The third determining module is configured to determine the abnormal data interval as the current monitoring data interval when the abnormal data interval meets the first preset condition.
With reference to the second aspect, in a first implementation manner of the second aspect of the embodiment of the present invention, the first determining module includes:
The first determining submodule is configured to determine whether there is a current monitoring data interval in response to receiving data;
The second determining submodule is configured to determine whether the data belongs to the current monitoring data interval when there is a current monitoring data interval;
Creating a submodule is configured to create a current monitoring data interval based on the received data when there is no current monitoring data interval.
With reference to the second aspect and the first implementation manner of the second aspect, in an embodiment of the present invention, in a second implementation manner of the second aspect, the device further includes:
The update module is configured to update the current monitoring data interval according to the data when the data belongs to the current monitoring data interval.
With reference to the second aspect, the first implementation manner of the second aspect, and the second implementation manner of the second aspect, in an embodiment of the present invention, in a third implementation manner of the second aspect, the update module include:
The first update submodule is configured to update the current monitoring data interval according to the data when the data belongs to the current monitoring data interval;
The first deletion submodule is configured to delete the abnormal data interval.
Combining the second aspect, the first implementation manner of the second aspect, the second implementation manner of the second aspect, and the third implementation manner of the second aspect, the embodiment of the present invention is located in the second aspect of the second aspect. In four implementations, the second determining module includes:
The third determining submodule is configured to determine whether there is an abnormal data interval matching the data when the data does not belong to the current monitoring data interval;
The second update submodule is configured to update the abnormal data interval according to the data when there is an abnormal data interval matching the data;
The first new submodule is configured to create a new abnormal data interval when there is no abnormal data interval matching the data.
Combining the second aspect, the first implementation of the second aspect, the second implementation of the second aspect, the third implementation of the second aspect, and the fourth implementation of the second aspect, In a fifth implementation manner of the second aspect of the embodiment of the present invention, the first newly created submodule includes:
The fourth determining submodule is configured to determine whether the number of abnormal data intervals is greater than a preset number threshold when there is no abnormal data interval matching the data;
The second newly created submodule is configured to delete the abnormal data interval satisfying the second preset condition and create a new abnormal data interval when the number of the abnormal data intervals is greater than the preset number threshold.
The third new submodule is configured to create a new abnormal data interval when the number of abnormal data intervals is not greater than the preset number threshold.
Combining the second aspect, the first implementation of the second aspect, the second implementation of the second aspect, the third implementation of the second aspect, and the fourth implementation of the second aspect and In a fifth implementation manner of the second aspect, in a sixth implementation manner of the second aspect of the embodiment of the present invention, the third determining module includes:
The fifth determining submodule is configured to determine the abnormal data interval as the current monitoring data interval when the abnormal data interval meets the first preset condition;
The second deletion submodule is configured to delete the abnormal data interval.
Combine the second aspect, the first implementation of the second aspect, the second implementation of the second aspect, the third implementation of the second aspect, and the fourth implementation of the second aspect, In a fifth implementation manner of the second aspect and a sixth implementation manner of the second aspect, in the seventh implementation manner of the second aspect of the embodiment of the present invention, the update module or update submodule is Configured to implement one or more of the following update operations:
Updating the central value of the interval of the current monitoring data interval or the abnormal data interval to the data, and updating the interval range of the current monitoring data interval or the abnormal data interval according to the preset interval length;
Update the number of data in the current monitoring data interval or abnormal data interval;
Update the data update time of the current monitoring data interval or the abnormal data interval.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, where the memory is used to store one or more computer instructions that support a data monitoring device to execute the data monitoring method in the first aspect The processor is configured to execute computer instructions stored in the memory. The data monitoring device may further include a communication interface for the data monitoring device to communicate with other devices or communication networks.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium for storing computer instructions used by a data monitoring device, which includes a method for performing the data monitoring method in the first aspect described above is a data monitoring device Computer instructions.
The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:
The above technical solution sets multiple data intervals, compares the newly received data with the multiple data intervals in sequence, and combines the characteristics of the data intervals to determine whether the newly received data belongs to normal data fluctuations. The technical solution can avoid the misjudgment of normal but large-scale data fluctuations, thereby achieving the purpose of accurately monitoring the data.
It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and do not limit the embodiments of the present invention.
下文中，將參考附圖詳細描述本發明實施例的示例性實施方式，以使本領域技術人員可容易地實現它們。此外，為了清楚起見，在附圖中省略了與描述示例性實施方式無關的部分。
在本發明實施例中，應理解，諸如“包括”或“具有”等的術語旨在指示本說明書中所公開的特徵、數字、步驟、行為、部件、部分或其組合的存在，並且不欲排除一個或多個其他特徵、數字、步驟、行為、部件、部分或其組合存在或被添加的可能性。
另外還需要說明的是，在不衝突的情況下，本發明中的實施例及實施例中的特徵可以相互組合。下面將參考附圖並結合實施例來詳細說明本發明實施例。
本發明實施例提供的技術方案通過設置多個資料區間，將新接收的資料與多個資料區間順序比對，並結合資料區間的特點來判斷新接收的資料是否屬於正常的資料波動。該技術方案能夠避免對於正常的、但幅度較大的資料波動出現誤判的情況，進而實現準確監控資料的目的。
圖1示出根據本發明一實施方式的資料監控方法的流程圖，如圖1所示，所述資料監控方法包括以下步驟S101-S103：
在步驟S101中，回應於接收到資料，確定所述資料是否屬於當前監控資料區間，其中，所述資料的接收頻率高於預設頻率臨限值；
在步驟S102中，當所述資料不屬於所述當前監控資料區間時，確定是否存在與所述資料匹配的異常資料區間，當存在與所述資料匹配的異常資料區間時，根據所述資料對所述異常資料區間進行更新；
在步驟S103中，當所述異常資料區間滿足第一預設條件時，將所述異常資料區間確定為當前監控資料區間。
上文提及，在對資料進行即時監控時，有可能會出現幅度較大的資料波動，這種資料波動有的是由於雜訊引起的，比如資料源發生錯誤或者資料產生行為出現異常，這種資料波動的特點是在資料出現突變後會回落正常；有的資料波動卻屬於正常的資料變動，這種資料波動的特點是資料在出現突變後不會回落，其體現了正常的資料走勢。但現有技術通常僅僅是通過比較新接收資料與相鄰的已接收資料之間的差值是否處於正常範圍來判斷新接收資料是否有效，是否屬於雜訊，若新接收資料與相鄰的已接收資料之間的差值處於正常範圍內，則判斷新接收資料有效，否則就認為新接收資料無效，屬於雜訊，這就會導致對於正常的、但幅度較大的資料波動出現誤判的情況，而在誤判之後還需要人為發現並實施人為的校正，很顯然，上述現有技術的處理方法會大大降低資料監控的準確性，對於資料的收集和監控產生非常不利的影響。
考慮到上述缺陷，在該實施方式中，提出一種資料監控方法，該方法在接收到資料後，首先確定所述資料是否屬於當前監控資料區間，其中，所述當前監控資料區間代表了當前時刻之前所接收的歷史資料所形成的歷史資料變化趨勢；若不屬於，則初步判斷新資料有可能偏離歷史資料變化趨勢，繼續確定是否存在與所述資料匹配的異常資料區間，其中，所述異常資料區間用於輔助判斷是否會出現新的資料變化趨勢；若存在，則根據所述資料對異常資料區間進行更新；最後，當異常資料區間滿足第一預設條件時，將異常資料區間確定為當前監控資料區間，此時新的資料變化趨勢產生。該技術方案通過設置多個資料區間，將新接收的資料與多個資料區間順序比對，並結合資料區間的特點來判斷新接收的資料是否屬於正常的資料波動。該技術方案能夠避免對於正常的、幅度較大的資料波動出現誤判的情況，進而實現準確監控資料的目的。
其中，所述資料的接收頻率高於預設頻率臨限值，也就是說，本實施方式對於高頻接收資料更加有效。
其中，所述異常資料區間的數量可以為1個、2個或者更多個，當然也可設置一個預設區間數量臨限值來限制異常資料區間的數量，使其不致過多，以影響資料變化趨勢統計的正確性。其中，當所述異常資料區間的數量為2個或多個時，所述異常資料區間之間可以存在部分重疊，也可以不存在重疊。
在本實施例的一個可選實現方式中，與所述資料匹配的異常資料區間指的是，所述資料落入所述異常資料區間的數值範圍內，比如，若某一異常資料區間的數值範圍為2-6，新接收的資料為5，5落入了範圍2-6，因此可以說該異常資料區間與新接收的資料相匹配，若新接收的資料為7，7不屬於範圍2-6，則認為該異常資料區間與新接收的資料不相匹配。
在本實施例的一個可選實現方式中，所述第一預設條件比如可以為：所述異常資料區間中的資料數量高於第一預設資料數量臨限值，也可以為所述異常資料區間的更新次數高於預設次數臨限值，當然所述第一預設條件也可以設置為其他條件，只要所述第一預設條件的設置能夠使得所述異常資料區間代表一定的、相對穩定的、新的資料變化趨勢即可。
其中，所述當前監控資料區間和異常資料區間的區間長度或者說所述當前監控資料區間和異常資料區間的數值範圍可以相同也可以不相同，具體可根據實際應用的需要進行設定，比如，若對當前監控資料區間的考慮比重更大一些，則可將異常資料區間的區間長度設置得大於當前監控資料區間的區間長度，這樣就使得新的資料變化趨勢在足夠穩定後才可以替代歷史資料變化趨勢。
舉例來說，假設當前監控資料區間為9-15，異常資料區間有3個，第一異常資料區間為2-8，第二異常資料區間為16-22，第三異常資料區間為24-30，第一預設資料數量臨限值為6，若新接收到的資料為18，判斷該資料不屬於當前監控資料區間9-15，但存在與其相匹配的第二異常資料區間16-22，則根據新資料18對第二異常資料區間進行更新，然後判斷第二異常資料區間中的資料數量是否大於6，若大於，則將第二異常資料區間16-22替換當前監控資料區間9-15，成為新的當前監控資料區間。
在本實施例的一個可選實現方式中，如圖2所示，所述步驟S101，即回應於接收到資料，確定所述資料是否屬於當前監控資料區間的步驟包括步驟S201-S203：
在步驟S201中，回應於接收到資料，確定是否存在當前監控資料區間；
在步驟S202中，當存在當前監控資料區間時，確定所述資料是否屬於所述當前監控資料區間；
在步驟S203中，當不存在當前監控資料區間時，根據接收到的資料創建當前監控資料區間。
在該實施方式中，當接收到新資料時，首先確定是否存在當前監控資料區間，若存在，繼續確定所述資料是否屬於所述當前監控資料區間，若不存在，則根據接收到的資料創建當前監控資料區間。其中，在創建當前監控資料區間時，可以以所述資料為區間中心值，以預設區間長度為長度來創建當前監控資料區間，並將創建時間作為該當前監控資料區間的更新時間。當然也可以採取其他區間創建方法，本發明對其不作具體限定。
在本實施例的一個可選實現方式中，如圖3所示，所述步驟S101，即回應於接收到資料，確定所述資料是否屬於當前監控資料區間的步驟之後，包括：當所述資料屬於所述當前監控資料區間時，根據所述資料對所述當前監控資料區間進行更新的步驟，即在該實施方式中，所述方法包括以下步驟S301-S304：
在步驟S301中，回應於接收到資料，確定所述資料是否屬於當前監控資料區間，其中，所述資料的接收頻率高於預設頻率臨限值；
在步驟S302中，當所述資料屬於所述當前監控資料區間時，根據所述資料對所述當前監控資料區間進行更新；
在步驟S303中，當所述資料不屬於所述當前監控資料區間時，確定是否存在與所述資料匹配的異常資料區間，當存在與所述資料匹配的異常資料區間時，根據所述資料對所述異常資料區間進行更新；
在步驟S304中，當所述異常資料區間滿足第一預設條件時，將所述異常資料區間確定為當前監控資料區間。
在該實施方式中，當判斷新接收的資料屬於當前監控資料區間時，認為新接收的資料仍然沿襲歷史資料變化趨勢，則根據新接收的資料對於所述當前監控資料區間進行更新。
在本實施例的一個可選實現方式中，如圖4所示，所述步驟S302，即當所述資料屬於所述當前監控資料區間時，根據所述資料對所述當前監控資料區間進行更新的步驟，包括步驟S401-S402：
在步驟S401中，當所述資料屬於所述當前監控資料區間時，根據所述資料對所述當前監控資料區間進行更新；
在步驟S402中，刪除異常資料區間。
在該實施方式中，當判斷新接收的資料屬於當前監控資料區間時，認為新接收的資料仍然沿襲歷史資料變化趨勢，而此時之前存在的一個或多個異常資料區間不再具有太多的參考價值，因此在根據新接收的資料對於所述當前監控資料區間進行更新之後，可以將異常資料區間全部刪除。
其中，根據資料對當前監控資料區間或異常資料區間進行的更新，包括以下更新操作中的一種或幾種：
將所述當前監控資料區間或異常資料區間的區間中心值更新為所述資料，並根據預設區間長度更新所述當前監控資料區間或異常資料區間的區間範圍，比如，假設當前監控資料區間或異常資料區間為9-15，中心值為12，預設區間長度為7，若新接收的資料為14，則對當前監控資料區間或異常資料區間進行更新後，新的當前監控資料區間或異常資料區間變為以新接收資料14為中心值，長度為7的區間11-17；
更新所述當前監控資料區間或異常資料區間中的資料數量；
更新所述當前監控資料區間或異常資料區間的資料更新時間，其中，所述資料更新時間用於表徵所述當前監控資料區間或異常資料區間的時效性。
在本實施例的一個可選實現方式中，如圖5所示，所述步驟S102，即當所述資料不屬於所述當前監控資料區間時，確定是否存在與所述資料匹配的異常資料區間，當存在與所述資料匹配的異常資料區間時，根據所述資料對所述異常資料區間進行更新的步驟，包括步驟S501-S503：
在步驟S501中，當所述資料不屬於所述當前監控資料區間時，確定是否存在與所述資料匹配的異常資料區間；
在步驟S502中，當存在與所述資料匹配的異常資料區間時，根據所述資料對所述異常資料區間進行更新；
在步驟S503中，當不存在與所述資料匹配的異常資料區間時，新建異常資料區間。
在該實施方式中，當確定新接收的資料不屬於當前監控資料區間時，首先確定是否存在與所述資料匹配的異常資料區間；進一步，若存在，則根據所述資料對所述異常資料區間進行更新；若不存在，則新建異常資料區間。其中，在新建異常資料區間時，與上文描述的當前監控資料區間的創建類似，可以以所述資料為區間中心值，以預設區間長度為長度來建立異常資料區間，並將新建時間作為所述異常資料區間的更新時間，也可以採取其他區間新建方法。
在本實施例的一個可選實現方式中，如圖6所示，所述步驟S503，即當不存在與所述資料匹配的異常資料區間時，新建異常資料區間的步驟，包括步驟S601-S603：
在步驟S601中，當不存在與所述資料匹配的異常資料區間時，確定所述異常資料區間的數量是否大於預設區間數量臨限值；
在步驟S602中，當所述異常資料區間的數量大於所述預設區間數量臨限值時，刪除滿足第二預設條件的異常資料區間，新建異常資料區間；
在步驟S603中，當所述異常資料區間的數量不大於所述預設區間數量臨限值時，新建異常資料區間。
在該實施方式中，考慮到異常資料區間的數量不宜過多，否則會影響資料變化趨勢統計的準確性，因此，當確定不存在與所述資料匹配的異常資料區間時，首先確定目前存在的異常資料區間的數量是否大於預設區間數量臨限值，若是，說明目前的異常資料區間數量過多，需刪除一個異常資料區間後再創建一個新的異常資料區間，若否，則直接創建一個新的異常資料區間。
其中，所述第二預設條件可以為以下條件中的一個或多個：
異常資料區間的更新時間早於預設時間臨限值，即刪除更新時間過於久遠的異常資料區間；
異常資料區間中的資料數量低於第二預設資料數量臨限值，即刪除資料數量較少的異常資料區間。
在本實施例的一個可選實現方式中，如圖7所示，所述步驟S103，即當所述異常資料區間滿足第一預設條件時，將所述異常資料區間確定為當前監控資料區間的步驟，包括步驟S701-S702：
在步驟S701中，當所述異常資料區間滿足第一預設條件時，將所述異常資料區間確定為當前監控資料區間；
在步驟S702中，刪除異常資料區間。
在該實施方式中，當判斷所述異常資料區間滿足第一預設條件時，認為所述異常資料區間已經足夠代表新的資料變化趨勢，其他異常資料區間都不再具有參考價值，因此在將所述異常資料區間替換當前監控資料區間成為新的當前監控資料區間之後，將其他異常資料區間全部刪除。
其中，所述預設頻率臨限值、預設區間數量臨限值、第一預設資料數量臨限值、預設次數臨限值、預設時間臨限值和第二預設資料數量臨限值可根據實際應用的需要進行設置，本發明對其不作具體限定。
下述為本發明裝置實施例，可以用於執行本發明方法實施例。
圖8示出根據本發明一實施方式的資料監控裝置的結構方塊圖，該裝置可以通過軟體軟體、硬體或者兩者的結合實現成為電子設備的部分或者全部。如圖8所示，所述資料監控裝置包括：
第一確定模組801，被配置為回應於接收到資料，確定所述資料是否屬於當前監控資料區間，其中，所述資料的接收頻率高於預設頻率臨限值；
第二確定模組802，被配置為當所述資料不屬於所述當前監控資料區間時，確定是否存在與所述資料匹配的異常資料區間，當存在與所述資料匹配的異常資料區間時，根據所述資料對所述異常資料區間進行更新；
第三確定模組803，被配置為當所述異常資料區間滿足第一預設條件時，將所述異常資料區間確定為當前監控資料區間。
上文提及，在對資料進行即時監控時，有可能會出現幅度較大的資料波動，這種資料波動有的是由於雜訊引起的，比如資料源發生錯誤或者資料產生行為出現異常，這種資料波動的特點是在資料出現突變後會回落正常；有的資料波動卻屬於正常的資料變動，這種資料波動的特點是資料在出現突變後不會回落，其體現了正常的資料走勢。但現有技術通常僅僅是通過比較新接收資料與相鄰的已接收資料之間的差值是否處於正常範圍來判斷新接收資料是否有效，是否屬於雜訊，若新接收資料與相鄰的已接收資料之間的差值處於正常範圍內，則判斷新接收資料有效，否則就認為新接收資料無效，屬於雜訊，這就會導致對於正常的、但幅度較大的資料波動出現誤判的情況，而在誤判之後還需要人為發現並實施人為的校正，很顯然，上述現有技術的處理方法會大大降低資料監控的準確性，對於資料的收集和監控產生非常不利的影響。
考慮到上述缺陷，在該實施方式中，提出一種資料監控裝置，在接收到資料後，該裝置中的第一確定模組801確定所述資料是否屬於當前監控資料區間，其中，所述當前監控資料區間代表了當前時刻之前所接收的歷史資料所形成的歷史資料變化趨勢；若不屬於，則初步判斷新資料有可能偏離歷史資料變化趨勢，第二確定模組802繼續確定是否存在與所述資料匹配的異常資料區間，其中，所述異常資料區間用於輔助判斷是否會出現新的資料變化趨勢；若存在，則根據所述資料對異常資料區間進行更新；當異常資料區間滿足第一預設條件時，第三確定模組803將異常資料區間確定為當前監控資料區間，此時新的資料變化趨勢產生。該技術方案通過設置多個資料區間，將新接收的資料與多個資料區間順序比對，並結合資料區間的特點來判斷新接收的資料是否屬於正常的資料波動。該技術方案能夠避免對於正常的、幅度較大的資料波動出現誤判的情況，進而實現準確監控資料的目的。
其中，所述資料的接收頻率高於預設頻率臨限值，也就是說，本實施方式對於高頻接收資料更加有效。
其中，所述異常資料區間的數量可以為1個、2個或者更多個，當然也可設置一個預設區間數量臨限值來限制異常資料區間的數量，使其不致過多，以影響資料變化趨勢統計的正確性。其中，當所述異常資料區間的數量為2個或多個時，所述異常資料區間之間可以存在部分重疊，也可以不存在重疊。
在本實施例的一個可選實現方式中，與所述資料匹配的異常資料區間指的是，所述資料落入所述異常資料區間的數值範圍內，比如，若某一異常資料區間的數值範圍為2-6，新接收的資料為5，5落入了範圍2-6，因此可以說該異常資料區間與新接收的資料相匹配，若新接收的資料為7，7不屬於範圍2-6，則認為該異常資料區間與新接收的資料不相匹配。
在本實施例的一個可選實現方式中，所述第一預設條件比如可以為：所述異常資料區間中的資料數量高於第一預設資料數量臨限值，也可以為所述異常資料區間的更新次數高於預設次數臨限值，當然所述第一預設條件也可以設置為其他條件，只要所述第一預設條件的設置能夠使得所述異常資料區間代表一定的、相對穩定的、新的資料變化趨勢即可。
其中，所述當前監控資料區間和異常資料區間的區間長度或者說所述當前監控資料區間和異常資料區間的數值範圍可以相同也可以不相同，具體可根據實際應用的需要進行設定，比如，若對當前監控資料區間的考慮比重更大一些，則可將異常資料區間的區間長度設置得大於當前監控資料區間的區間長度，這樣就使得新的資料變化趨勢在足夠穩定後才可以替代歷史資料變化趨勢。
舉例來說，假設當前監控資料區間為9-15，異常資料區間有3個，第一異常資料區間為2-8，第二異常資料區間為16-22，第三異常資料區間為24-30，第一預設資料數量臨限值為6，若新接收到的資料為18，判斷該資料不屬於當前監控資料區間9-15，但存在與其相匹配的第二異常資料區間16-22，則根據新資料18對第二異常資料區間進行更新，然後判斷第二異常資料區間中的資料數量是否大於6，若大於，則將第二異常資料區間16-22替換當前監控資料區間9-15，成為新的當前監控資料區間。
在本實施例的一個可選實現方式中，如圖9所示，所述第一確定模組801包括：
第一確定子模組901，被配置為回應於接收到資料，確定是否存在當前監控資料區間；
第二確定子模組902，被配置為當存在當前監控資料區間時，確定所述資料是否屬於所述當前監控資料區間；
創建子模組903，被配置為當不存在當前監控資料區間時，根據接收到的資料創建當前監控資料區間。
在該實施方式中，當接收到新資料時，第一確定子模組901確定是否存在當前監控資料區間，若存在，第二確定子模組902繼續確定所述資料是否屬於所述當前監控資料區間，若不存在，則創建子模組903根據接收到的資料創建當前監控資料區間。其中，在創建子模組903創建當前監控資料區間時，可以以所述資料為區間中心值，以預設區間長度為長度來創建當前監控資料區間，並將創建時間作為該當前監控資料區間的更新時間。當然也可以採取其他區間創建方法，本發明對其不作具體限定。
在本實施例的一個可選實現方式中，如圖10所示，所述裝置還包括更新模組，即在該實施方式中，所述裝置包括：
第一確定模組1001，被配置為回應於接收到資料，確定所述資料是否屬於當前監控資料區間，其中，所述資料的接收頻率高於預設頻率臨限值；
更新模組1002，被配置為當所述資料屬於所述當前監控資料區間時，根據所述資料對所述當前監控資料區間進行更新
第二確定模組1003，被配置為當所述資料不屬於所述當前監控資料區間時，確定是否存在與所述資料匹配的異常資料區間，當存在與所述資料匹配的異常資料區間時，根據所述資料對所述異常資料區間進行更新；
第三確定模組1004，被配置為當所述異常資料區間滿足第一預設條件時，將所述異常資料區間確定為當前監控資料區間。
在該實施方式中，當判斷新接收的資料屬於當前監控資料區間時，認為新接收的資料仍然沿襲歷史資料變化趨勢，則更新模組1002根據新接收的資料對於所述當前監控資料區間進行更新。
在本實施例的一個可選實現方式中，如圖11所示，所述更新模組1002包括：
第一更新子模組1101，被配置為當所述資料屬於所述當前監控資料區間時，根據所述資料對所述當前監控資料區間進行更新；
第一刪除子模組1102，被配置為刪除異常資料區間。
在該實施方式中，當判斷新接收的資料屬於當前監控資料區間時，認為新接收的資料仍然沿襲歷史資料變化趨勢，而此時之前存在的一個或多個異常資料區間不再具有太多的參考價值，因此在第一更新子模組1101根據新接收的資料對於所述當前監控資料區間進行更新之後，第一刪除子模組1102可以將異常資料區間全部刪除。
其中，所述更新模組或更新子模組被配置為實現以下更新操作中的一種或幾種：
將所述當前監控資料區間或異常資料區間的區間中心值更新為所述資料，並根據預設區間長度更新所述當前監控資料區間或異常資料區間的區間範圍，比如，假設當前監控資料區間或異常資料區間為9-15，中心值為12，預設區間長度為7，若新接收的資料為14，則對當前監控資料區間或異常資料區間進行更新後，新的當前監控資料區間或異常資料區間變為以新接收資料14為中心值，長度為7的區間11-17；
更新所述當前監控資料區間或異常資料區間中的資料數量；
更新所述當前監控資料區間或異常資料區間的資料更新時間，其中，所述資料更新時間用於表徵所述當前監控資料區間或異常資料區間的時效性。
在本實施例的一個可選實現方式中，如圖12所示，所述第二確定模組802包括：
第三確定子模組1201，被配置為當所述資料不屬於所述當前監控資料區間時，確定是否存在與所述資料匹配的異常資料區間；
第二更新子模組1202，被配置為當存在與所述資料匹配的異常資料區間時，根據所述資料對所述異常資料區間進行更新；
第一新建子模組1203，被配置為當不存在與所述資料匹配的異常資料區間時，新建異常資料區間。
在該實施方式中，當確定新接收的資料不屬於當前監控資料區間時，第三確定子模組1201確定是否存在與所述資料匹配的異常資料區間；進一步，若存在，第二更新子模組1202根據所述資料對所述異常資料區間進行更新；若不存在，則第一新建子模組1203新建異常資料區間。其中，在第一新建子模組1203新建異常資料區間時，與上文描述的當前監控資料區間的創建類似，可以以所述資料為區間中心值，以預設區間長度為長度來建立異常資料區間，並將新建時間作為所述異常資料區間的更新時間，也可以採取其他區間新建方法。
在本實施例的一個可選實現方式中，如圖13所示，所述第一新建子模組1203包括：
第四確定子模組1301，被配置為當不存在與所述資料匹配的異常資料區間時，確定所述異常資料區間的數量是否大於預設數量臨限值；
第二新建子模組1302，被配置為當所述異常資料區間的數量大於所述預設數量臨限值時，刪除滿足第二預設條件的異常資料區間，新建異常資料區間；
第三新建子模組1303，被配置為當所述異常資料區間的數量不大於所述預設數量臨限值時，新建異常資料區間。
在該實施方式中，考慮到異常資料區間的數量不宜過多，否則會影響資料變化趨勢統計的準確性，因此，當確定不存在與所述資料匹配的異常資料區間時，第四確定子模組1301確定目前存在的異常資料區間的數量是否大於預設區間數量臨限值，若是，說明目前的異常資料區間數量過多，第二新建子模組1302刪除一個異常資料區間後再創建一個新的異常資料區間，若否，第三新建子模組1303直接創建一個新的異常資料區間。
其中，所述第二預設條件可以為以下條件中的一個或多個：
異常資料區間的更新時間早於預設時間臨限值，即刪除更新時間過於久遠的異常資料區間；
異常資料區間中的資料數量低於第二預設資料數量臨限值，即刪除資料數量較少的異常資料區間。
在本實施例的一個可選實現方式中，如圖14所示，所述第三確定模組803包括：
第五確定子模組1401，被配置為當所述異常資料區間滿足第一預設條件時，將所述異常資料區間確定為當前監控資料區間；
第二刪除子模組1402，被配置為刪除異常資料區間。
在該實施方式中，當判斷所述異常資料區間滿足第一預設條件時，認為所述異常資料區間已經足夠代表新的資料變化趨勢，其他異常資料區間都不再具有參考價值，因此在第五確定子模組1401將所述異常資料區間替換當前監控資料區間成為新的當前監控資料區間之後，第二刪除子模組1402將其他異常資料區間全部刪除。
其中，所述預設頻率臨限值、預設區間數量臨限值、第一預設資料數量臨限值、預設次數臨限值、預設時間臨限值和第二預設資料數量臨限值可根據實際應用的需要進行設置，本發明對其不作具體限定。
本發明實施例還公開了一種電子設備，圖15示出根據本發明一實施方式的電子設備的結構方塊圖，如圖15所示，所述電子設備1500包括記憶體1501和處理器1502；其中，
所述記憶體1501用於儲存一條或多條電腦指令，其中，所述一條或多條電腦指令被所述處理器1502執行以實現上述任一方法步驟。
圖16適於用來實現根據本發明實施方式的資料監控方法的電腦系統的結構示意圖。
如圖16所示，電腦系統1600包括中央處理單元(CPU)1601，其可以根據儲存在唯讀記憶體(ROM)1602中的程式或者從儲存部分1608加載到隨機存取記憶體(RAM)1603中的程式而執行上述實施方式中的各種處理。在RAM1603中，還儲存有系統1600操作所需的各種程式和資料。CPU1601、ROM1602以及RAM1603通過匯流排1604彼此相連。輸入/輸出(I/O)介面1605也連接至匯流排1604。
以下部件連接至I/O介面1605：包括鍵盤、滑鼠等的輸入部分1606；包括諸如陰極射線管(CRT)、液晶顯示器(LCD)等以及揚聲器等的輸出部分1607；包括硬碟等的儲存部分1608；以及包括諸如LAN卡、調變解調器等的網路介面卡的通信部分1609。通信部分1609經由諸如網際網路的網路執行通信處理。驅動器1610也根據需要連接至I/O介面1605。可拆卸媒體1611，諸如磁碟、光碟、磁光碟、半導體記憶體等等，根據需要安裝在驅動器1610上，以便於從其上讀出的電腦程式根據需要被安裝入儲存部分1608。
特別地，根據本發明的實施方式，上文描述的方法可以被實現為電腦軟體軟體程式。例如，本發明的實施方式包括一種電腦程式產品，其包括有形地包含在及其可讀媒體上的電腦程式，所述電腦程式包含用於執行所述資料監控方法的程式碼。在這樣的實施方式中，該電腦程式可以通過通信部分1609從網路上被下載和安裝，及/或從可拆卸媒體1611被安裝。
附圖中的流程圖和方塊圖，圖示了按照本發明各種實施方式的系統、方法和電腦程式產品的可能實現的體系架構、功能和操作。在這點上，路程圖或方塊圖中的每個方塊可以代表一個模組、程式段或程式碼的一部分，所述模組、程式段或程式碼的一部分包含一個或多個用於實現規定的邏輯功能的可執行指令。也應當注意，在有些作為替換的實現中，方塊中所標注的功能也可以以不同於附圖中所標注的順序發生。例如，兩個接連地表示的方塊實際上可以基本平行地執行，它們有時也可以按相反的順序執行，這依所涉及的功能而定。也要注意的是，方塊圖及/或流程圖中的每個方塊、以及方塊圖及/或流程圖中的方塊的組合，可以用執行規定的功能或操作的專用的基於硬體的系統來實現，或者可以用專用硬體與電腦指令的組合來實現。
描述於本發明實施方式中所涉及到的單元或模組可以通過軟體軟體的方式實現，也可以通過硬體的方式來實現。所描述的單元或模組也可以設置在處理器中，這些單元或模組的名稱在某種情況下並不構成對該單元或模組本身的限定。
作為另一態樣，本發明實施例還提供了一種電腦可讀儲存媒體，該電腦可讀儲存媒體可以是上述實施方式中所述裝置中所包含的電腦可讀儲存媒體；也可以是單獨存在，未裝配入設備中的電腦可讀儲存媒體。電腦可讀儲存媒體儲存有一個或者一個以上程式，所述程式被一個或者一個以上的處理器用來執行描述於本發明實施例的方法。
以上描述僅為本發明的較佳實施例以及對所運用技術原理的說明。本領域技術人員應當理解，本發明實施例中所涉及的發明範圍，並不限於上述技術特徵的特定組合而成的技術方案，同時也應涵蓋在不脫離所述發明構思的情況下，由上述技術特徵或其等同特徵進行任意組合而形成的其它技術方案。例如上述特徵與本發明實施例中公開的(但不限於)具有類似功能的技術特徵進行互相替換而形成的技術方案。
Below, Exemplary implementations of embodiments of the invention will be described in detail with reference to the drawings, So that those skilled in the art can easily implement them. In addition, For clarity, Parts not related to the description of the exemplary embodiment are omitted in the drawings.
In the embodiment of the present invention, It should be understood that Terms such as "include" or "have" are intended to indicate the features disclosed in this specification, digital, step, behavior, part, The existence of parts or combinations thereof, And do not want to exclude one or more other features, digital, step, behavior, part, The possibility that some or a combination exists or is added.
Another thing to note is that Without conflict, The embodiments in the present invention and the features in the embodiments can be combined with each other. The embodiments of the present invention will be described in detail below with reference to the drawings and in conjunction with the embodiments.
The technical solution provided by the embodiments of the present invention sets multiple data intervals, Compare the newly received data with multiple data intervals sequentially, Combined with the characteristics of the data interval to determine whether the newly received data belongs to normal data fluctuations. This technical solution can avoid However, there is a misjudgment in the data fluctuation with a large range, Then achieve the purpose of accurate monitoring data.
FIG. 1 shows a flowchart of a data monitoring method according to an embodiment of the present invention, As shown in Figure 1, The data monitoring method includes the following steps S101-S103:
In step S101, In response to receiving the data, Determine whether the data belongs to the current monitoring data interval, among them, The receiving frequency of the data is higher than the preset frequency threshold;
In step S102, When the data does not belong to the current monitoring data interval, Determine whether there is an abnormal data interval that matches the data, When there is an abnormal data interval matching the data, Update the abnormal data interval according to the data;
In step S103, When the abnormal data interval meets the first preset condition, The abnormal data interval is determined as the current monitoring data interval.
As mentioned above, In real-time monitoring of data, There may be large data fluctuations, Some of this data fluctuation is caused by noise, For example, an error in the data source or an abnormal behavior in the data generation, The characteristic of this kind of data fluctuation is that it will fall back to normal after the data appear abruptly; Some data fluctuations are normal data changes, The characteristic of this kind of data fluctuation is that the data will not fall back after a sudden change, It reflects the normal data trend. However, the existing technology usually judges whether the newly received data is valid only by comparing whether the difference between the newly received data and the adjacent received data is within a normal range, Whether it is noise, If the difference between the newly received data and the adjacent received data is within the normal range, Judge that the newly received data is valid, Otherwise, the newly received data is considered invalid. Is noise, This will lead to normal, However, there is a misjudgment in the data fluctuation with a large range, After the misjudgment, it is necessary to find and implement artificial correction, obviously, The above-mentioned prior art processing methods will greatly reduce the accuracy of data monitoring, It has a very adverse effect on the collection and monitoring of data.
Considering the above defects, In this embodiment, Propose a data monitoring method, After the method receives the data, First determine whether the data belongs to the current monitoring data interval, among them, The current monitoring data interval represents a historical data change trend formed by historical data received before the current time; If it does not belong, Preliminary judgment that new data may deviate from the trend of historical data, Continue to determine whether there is an abnormal data interval that matches the data, among them, The abnormal data interval is used to assist in determining whether a new data change trend will occur; If it exists, Update the abnormal data interval according to the data; At last, When the abnormal data interval meets the first preset condition, Determine the abnormal data interval as the current monitoring data interval, At this time, new data change trends are generated. The technical solution sets multiple data intervals, Compare the newly received data with multiple data intervals sequentially, Combined with the characteristics of the data interval to determine whether the newly received data belongs to normal data fluctuations. This technical solution can avoid There is a misjudgment in the case of large data fluctuation, Then achieve the purpose of accurate monitoring data.
among them, The receiving frequency of the data is higher than the preset frequency threshold, In other words, This embodiment is more effective for high-frequency reception data.
among them, The number of the abnormal data intervals can be one, 2 or more, Of course, you can also set a preset threshold for the number of intervals to limit the number of abnormal data intervals, So as not to overdo it, In order to affect the accuracy of the statistics of data change trends. among them, When the number of the abnormal data intervals is 2 or more, There may be partial overlap between the abnormal data intervals, There may be no overlap.
In an optional implementation of this embodiment, The anomalous data interval matching the data refers to, The data falls within the numerical range of the abnormal data interval, such as, If the value range of an abnormal data interval is 2-6, The newly received data is 5, 5 falls into the range 2-6, Therefore, it can be said that the abnormal data interval matches the newly received data, If the newly received data is 7, 7 is not in the range 2-6, It is considered that the abnormal data interval does not match the newly received data.
In an optional implementation of this embodiment, The first preset condition may be, for example: The amount of data in the abnormal data interval is higher than the first preset amount of data threshold, It may also be that the number of updates of the abnormal data interval is higher than the preset number of thresholds, Of course, the first preset condition may also be set to other conditions, As long as the setting of the first preset condition enables the abnormal data interval to represent a certain, Relatively stable, The new data change trend is enough.
among them, The length of the interval between the current monitoring data interval and the abnormal data interval or the value range of the current monitoring data interval and the abnormal data interval may or may not be the same, It can be set according to the needs of the actual application, such as, If the current monitoring data interval is considered more important, Then the interval length of the abnormal data interval can be set larger than the interval length of the current monitoring data interval, In this way, the new data change trend can only replace the historical data change trend after it is stable enough.
for example, Assuming that the current monitoring data interval is 9-15, There are 3 abnormal data intervals, The first anomaly data interval is 2-8, The second anomaly data interval is 16-22, The third anomaly data interval is 24-30, The first preset data quantity threshold is 6, If the newly received data is 18, Judging that the data does not belong to the current monitoring data interval 9-15, But there is a second anomaly data interval 16-22 matching it, Then update the second abnormal data interval according to the new data 18, Then determine whether the number of data in the second abnormal data interval is greater than 6, If greater than, Then replace the second abnormal data interval 16-22 with the current monitoring data interval 9-15, Become the new current monitoring data interval.
In an optional implementation of this embodiment, as shown in picture 2, In step S101, In response to receiving data, The step of determining whether the data belongs to the current monitoring data interval includes steps S201-S203:
In step S201, In response to receiving the data, Determine whether there is a current monitoring data interval;
In step S202, When there is a current monitoring data interval, Determine whether the data belongs to the current monitoring data interval;
In step S203, When there is no current monitoring data interval, Create the current monitoring data interval based on the received data.
In this embodiment, When new information is received, First determine whether there is a current monitoring data interval, If it exists, Continue to determine whether the data belongs to the current monitoring data interval, If it does not exist, Then the current monitoring data interval is created based on the received data. among them, When creating the current monitoring data interval, You can use the data as the center value of the interval, Use the preset interval length as the length to create the current monitoring data interval, The creation time is taken as the update time of the current monitoring data interval. Of course, you can also take other methods to create the interval, The present invention does not specifically limit it.
In an optional implementation of this embodiment, As shown in Figure 3, In step S101, In response to receiving data, After the step of determining whether the data belongs to the current monitoring data interval, include: When the data belongs to the current monitoring data interval, The step of updating the current monitoring data interval according to the data, That is, in this embodiment, The method includes the following steps S301-S304:
In step S301, In response to receiving the data, Determine whether the data belongs to the current monitoring data interval, among them, The receiving frequency of the data is higher than the preset frequency threshold;
In step S302, When the data belongs to the current monitoring data interval, Update the current monitoring data interval according to the data;
In step S303, When the data does not belong to the current monitoring data interval, Determine whether there is an abnormal data interval that matches the data, When there is an abnormal data interval matching the data, Update the abnormal data interval according to the data;
In step S304, When the abnormal data interval meets the first preset condition, The abnormal data interval is determined as the current monitoring data interval.
In this embodiment, When judging that the newly received data belongs to the current monitoring data interval, Considering that the newly received data still follows the trend of historical data changes, Then update the current monitoring data interval according to the newly received data.
In an optional implementation of this embodiment, As shown in Figure 4, In step S302, That is, when the data belongs to the current monitoring data interval, The step of updating the current monitoring data interval according to the data, Including steps S401-S402:
In step S401, When the data belongs to the current monitoring data interval, Update the current monitoring data interval according to the data;
In step S402, Delete the abnormal data interval.
In this embodiment, When judging that the newly received data belongs to the current monitoring data interval, Considering that the newly received data still follows the trend of historical data changes, However, the one or more abnormal data intervals that existed before this time no longer have much reference value, Therefore, after updating the current monitoring data interval according to the newly received data, You can delete all abnormal data intervals.
among them, Update the current monitoring data interval or abnormal data interval according to the data, Include one or more of the following update operations:
Update the central value of the interval of the current monitoring data interval or the abnormal data interval to the data, And update the interval range of the current monitoring data interval or the abnormal data interval according to the preset interval length, such as, Assuming that the current monitoring data interval or abnormal data interval is 9-15, The central value is 12, The preset interval length is 7, If the newly received data is 14, After updating the current monitoring data interval or abnormal data interval, The new current monitoring data interval or abnormal data interval becomes centered on the newly received data 14, The interval 11-17 of length 7;
Update the number of data in the current monitoring data interval or abnormal data interval;
Update the data update time of the current monitoring data interval or abnormal data interval, among them, The data update time is used to characterize the timeliness of the current monitoring data interval or the abnormal data interval.
In an optional implementation of this embodiment, As shown in Figure 5, In step S102, That is, when the data does not belong to the current monitoring data interval, Determine whether there is an abnormal data interval that matches the data, When there is an abnormal data interval matching the data, The step of updating the abnormal data interval according to the data, Including steps S501-S503:
In step S501, When the data does not belong to the current monitoring data interval, Determine whether there is an abnormal data interval matching the data;
In step S502, When there is an abnormal data interval matching the data, Update the abnormal data interval according to the data;
In step S503, When there is no abnormal data interval matching the data, Create anomaly data interval.
In this embodiment, When it is determined that the newly received data does not belong to the current monitoring data interval, First determine whether there is an abnormal data interval that matches the data; further, If it exists, Updating the abnormal data interval according to the data; If it does not exist, Then create anomalous data interval. among them, When creating a new anomaly data interval, Similar to the creation of the current monitoring data interval described above, You can use the data as the center value of the interval, Use the default interval length as the length to create the abnormal data interval, And take the newly created time as the update time of the abnormal data interval, You can also use other methods to create new intervals.
In an optional implementation of this embodiment, As shown in Figure 6, In step S503, That is, when there is no abnormal data interval matching the data, Steps for creating anomalous data intervals, Including steps S601-S603:
In step S601, When there is no abnormal data interval matching the data, Determine whether the number of the abnormal data intervals is greater than the preset number of thresholds;
In step S602, When the number of the abnormal data intervals is greater than the preset number of thresholds, Delete the abnormal data interval that satisfies the second preset condition, Newly created abnormal data interval;
In step S603, When the number of the abnormal data intervals is not greater than the preset number of thresholds, Create anomaly data interval.
In this embodiment, Considering that the number of abnormal data intervals should not be excessive, Otherwise, it will affect the accuracy of the statistics of data change trends, therefore, When it is determined that there is no abnormal data interval matching the data, First determine whether the number of current abnormal data intervals is greater than the preset number of thresholds, if, It means that there are too many current abnormal data intervals, You need to delete an abnormal data interval and then create a new abnormal data interval, If not, Then a new abnormal data interval is directly created.
among them, The second preset condition may be one or more of the following conditions:
The update time of the abnormal data interval is earlier than the preset time threshold, That is to delete the abnormal data interval whose update time is too long;
The number of data in the abnormal data interval is lower than the second preset data number threshold, That is, the abnormal data interval with a small amount of data is deleted.
In an optional implementation of this embodiment, As shown in Figure 7, In step S103, That is, when the abnormal data interval meets the first preset condition, The step of determining the abnormal data interval as the current monitoring data interval, Including steps S701-S702:
In step S701, When the abnormal data interval meets the first preset condition, Determine the abnormal data interval as the current monitoring data interval;
In step S702, Delete the abnormal data interval.
In this embodiment, When it is judged that the abnormal data interval meets the first preset condition, Considering that the abnormal data interval is sufficient to represent the new data change trend, Other anomalous data intervals are no longer of reference value, Therefore, after replacing the current monitoring data interval with the abnormal data interval to become a new current monitoring data interval, Delete all other abnormal data intervals.
among them, The preset frequency threshold, The preset number of thresholds, The first preset data quantity threshold, Preset times threshold, The preset time threshold and the second preset data quantity threshold can be set according to actual application needs, The present invention does not specifically limit it.
The following is an embodiment of the device of the present invention, It can be used to implement the method embodiments of the present invention.
8 is a block diagram showing the structure of a data monitoring device according to an embodiment of the present invention, The device can be accessed through software, The hardware or a combination of the two is implemented as part or all of the electronic device. As shown in Figure 8, The data monitoring device includes:
The first determination module 801, Is configured to respond to received data, Determine whether the data belongs to the current monitoring data interval, among them, The receiving frequency of the data is higher than the preset frequency threshold;
Second determining module 802, Configured to when the data does not belong to the current monitoring data interval, Determine whether there is an abnormal data interval that matches the data, When there is an abnormal data interval matching the data, Update the abnormal data interval according to the data;
The third determination module 803, Is configured to when the abnormal data interval meets the first preset condition, The abnormal data interval is determined as the current monitoring data interval.
As mentioned above, In real-time monitoring of data, There may be large data fluctuations, Some of this data fluctuation is caused by noise, For example, an error in the data source or an abnormal behavior in the data generation, The characteristic of this kind of data fluctuation is that it will fall back to normal after the data appear abruptly; Some data fluctuations are normal data changes, The characteristic of this kind of data fluctuation is that the data will not fall back after a sudden change, It reflects the normal data trend. However, the existing technology usually judges whether the newly received data is valid only by comparing whether the difference between the newly received data and the adjacent received data is within a normal range, Whether it is noise, If the difference between the newly received data and the adjacent received data is within the normal range, Judge that the newly received data is valid, Otherwise, the newly received data is considered invalid. Is noise, This will lead to normal, However, there is a misjudgment in the data fluctuation with a large range, After the misjudgment, it is necessary to find and implement artificial correction, obviously, The above-mentioned prior art processing methods will greatly reduce the accuracy of data monitoring, It has a very adverse effect on the collection and monitoring of data.
Considering the above defects, In this embodiment, Propose a data monitoring device, After receiving the information, The first determination module 801 in the device determines whether the data belongs to the current monitoring data interval, among them, The current monitoring data interval represents a historical data change trend formed by historical data received before the current time; If it does not belong, Preliminary judgment that new data may deviate from the trend of historical data, The second determination module 802 continues to determine whether there is an abnormal data interval matching the data, among them, The abnormal data interval is used to assist in determining whether a new data change trend will occur; If it exists, Update the abnormal data interval according to the data; When the abnormal data interval meets the first preset condition, The third determining module 803 determines the abnormal data interval as the current monitoring data interval, At this time, new data change trends are generated. The technical solution sets multiple data intervals, Compare the newly received data with multiple data intervals sequentially, Combined with the characteristics of the data interval to determine whether the newly received data belongs to normal data fluctuations. This technical solution can avoid There is a misjudgment in the case of large data fluctuation, Then achieve the purpose of accurate monitoring data.
among them, The receiving frequency of the data is higher than the preset frequency threshold, In other words, This embodiment is more effective for high-frequency reception data.
among them, The number of the abnormal data intervals can be one, 2 or more, Of course, you can also set a preset threshold for the number of intervals to limit the number of abnormal data intervals, So as not to overdo it, In order to affect the accuracy of the statistics of data change trends. among them, When the number of the abnormal data intervals is 2 or more, There may be partial overlap between the abnormal data intervals, There may be no overlap.
In an optional implementation of this embodiment, The anomalous data interval matching the data refers to, The data falls within the numerical range of the abnormal data interval, such as, If the value range of an abnormal data interval is 2-6, The newly received data is 5, 5 falls into the range 2-6, Therefore, it can be said that the abnormal data interval matches the newly received data, If the newly received data is 7, 7 is not in the range 2-6, It is considered that the abnormal data interval does not match the newly received data.
In an optional implementation of this embodiment, The first preset condition may be, for example: The amount of data in the abnormal data interval is higher than the first preset amount of data threshold, It may also be that the number of updates of the abnormal data interval is higher than the preset number of thresholds, Of course, the first preset condition may also be set to other conditions, As long as the setting of the first preset condition enables the abnormal data interval to represent a certain, Relatively stable, The new data change trend is enough.
among them, The length of the interval between the current monitoring data interval and the abnormal data interval or the value range of the current monitoring data interval and the abnormal data interval may or may not be the same, It can be set according to the needs of the actual application, such as, If the current monitoring data interval is considered more important, Then the interval length of the abnormal data interval can be set larger than the interval length of the current monitoring data interval, In this way, the new data change trend can only replace the historical data change trend after it is stable enough.
for example, Assuming that the current monitoring data interval is 9-15, There are 3 abnormal data intervals, The first anomaly data interval is 2-8, The second anomaly data interval is 16-22, The third anomaly data interval is 24-30, The first preset data quantity threshold is 6, If the newly received data is 18, Judging that the data does not belong to the current monitoring data interval 9-15, But there is a second anomaly data interval 16-22 matching it, Then update the second abnormal data interval according to the new data 18, Then determine whether the number of data in the second abnormal data interval is greater than 6, If greater than, Then replace the second abnormal data interval 16-22 with the current monitoring data interval 9-15, Become the new current monitoring data interval.
In an optional implementation of this embodiment, As shown in Figure 9, The first determining module 801 includes:
The first determining submodule 901, Is configured to respond to received data, Determine whether there is a current monitoring data interval;
The second determination submodule 902, It is configured that when there is a current monitoring data interval, Determine whether the data belongs to the current monitoring data interval;
Create submodule 903, It is configured that when there is no current monitoring data interval, Create the current monitoring data interval based on the received data.
In this embodiment, When new information is received, The first determining submodule 901 determines whether there is a current monitoring data interval, If it exists, The second determination submodule 902 continues to determine whether the data belongs to the current monitoring data interval, If it does not exist, Then, the creation submodule 903 creates the current monitoring data interval according to the received data. among them, When creating the submodule 903 to create the current monitoring data interval, You can use the data as the center value of the interval, Use the preset interval length as the length to create the current monitoring data interval, The creation time is taken as the update time of the current monitoring data interval. Of course, you can also take other methods to create the interval, The present invention does not specifically limit it.
In an optional implementation of this embodiment, As shown in Figure 10, The device also includes an update module, That is, in this embodiment, The device includes:
The first determination module 1001, Is configured to respond to received data, Determine whether the data belongs to the current monitoring data interval, among them, The receiving frequency of the data is higher than the preset frequency threshold;
Update module 1002, Is configured to when the data belongs to the current monitoring data interval, Update the current monitoring data interval according to the data
The second determination module 1003, Configured to when the data does not belong to the current monitoring data interval, Determine whether there is an abnormal data interval that matches the data, When there is an abnormal data interval matching the data, Update the abnormal data interval according to the data;
The third determination module 1004, Is configured to when the abnormal data interval meets the first preset condition, The abnormal data interval is determined as the current monitoring data interval.
In this embodiment, When judging that the newly received data belongs to the current monitoring data interval, Considering that the newly received data still follows the trend of historical data changes, Then, the update module 1002 updates the current monitoring data interval according to the newly received data.
In an optional implementation of this embodiment, As shown in Figure 11, The update module 1002 includes:
The first update submodule 1101, Is configured to when the data belongs to the current monitoring data interval, Update the current monitoring data interval according to the data;
The first deletion submodule 1102, It is configured to delete the abnormal data interval.
In this embodiment, When judging that the newly received data belongs to the current monitoring data interval, Considering that the newly received data still follows the trend of historical data changes, However, the one or more abnormal data intervals that existed before this time no longer have much reference value, Therefore, after the first update submodule 1101 updates the current monitoring data interval according to the newly received data, The first deletion submodule 1102 can delete all abnormal data intervals.
among them, The update module or update submodule is configured to implement one or more of the following update operations:
Update the central value of the interval of the current monitoring data interval or the abnormal data interval to the data, And update the interval range of the current monitoring data interval or the abnormal data interval according to the preset interval length, such as, Assuming that the current monitoring data interval or abnormal data interval is 9-15, The central value is 12, The preset interval length is 7, If the newly received data is 14, After updating the current monitoring data interval or abnormal data interval, The new current monitoring data interval or abnormal data interval becomes centered on the newly received data 14, The interval 11-17 of length 7;
Update the number of data in the current monitoring data interval or abnormal data interval;
Update the data update time of the current monitoring data interval or abnormal data interval, among them, The data update time is used to characterize the timeliness of the current monitoring data interval or the abnormal data interval.
In an optional implementation of this embodiment, As shown in Figure 12, The second determining module 802 includes:
The third determination submodule 1201, Configured to when the data does not belong to the current monitoring data interval, Determine whether there is an abnormal data interval matching the data;
The second update submodule 1202, It is configured that when there is an abnormal data interval matching the data, Update the abnormal data interval according to the data;
The first new submodule 1203, It is configured that when there is no abnormal data interval matching the data, Create anomaly data interval.
In this embodiment, When it is determined that the newly received data does not belong to the current monitoring data interval, The third determining submodule 1201 determines whether there is an abnormal data interval matching the data; further, If it exists, The second update submodule 1202 updates the abnormal data interval according to the data; If it does not exist, Then, the first newly created sub-module 1203 creates an abnormal data interval. among them, When creating an abnormal data interval in the first newly created submodule 1203, Similar to the creation of the current monitoring data interval described above, You can use the data as the center value of the interval, Use the default interval length as the length to create the abnormal data interval, And take the newly created time as the update time of the abnormal data interval, You can also use other methods to create new intervals.
In an optional implementation of this embodiment, As shown in Figure 13, The first newly created sub-module 1203 includes:
The fourth determination submodule 1301, It is configured that when there is no abnormal data interval matching the data, Determine whether the number of the abnormal data intervals is greater than the preset number threshold;
The second new submodule 1302, It is configured that when the number of the abnormal data intervals is greater than the preset number threshold, Delete the abnormal data interval that satisfies the second preset condition, Newly created abnormal data interval;
The third new submodule 1303, It is configured that when the number of the abnormal data intervals is not greater than the preset number threshold, Create anomaly data interval.
In this embodiment, Considering that the number of abnormal data intervals should not be excessive, Otherwise, it will affect the accuracy of the statistics of data change trends, therefore, When it is determined that there is no abnormal data interval matching the data, The fourth determination submodule 1301 determines whether the number of currently existing abnormal data intervals is greater than the preset number of thresholds, if, It means that there are too many current abnormal data intervals, The second new submodule 1302 deletes an abnormal data interval and then creates a new abnormal data interval, If not, The third new submodule 1303 directly creates a new abnormal data interval.
among them, The second preset condition may be one or more of the following conditions:
The update time of the abnormal data interval is earlier than the preset time threshold, That is to delete the abnormal data interval whose update time is too long;
The number of data in the abnormal data interval is lower than the second preset data number threshold, That is, the abnormal data interval with a small amount of data is deleted.
In an optional implementation of this embodiment, As shown in Figure 14, The third determining module 803 includes:
The fifth determination submodule 1401, Is configured to when the abnormal data interval meets the first preset condition, Determine the abnormal data interval as the current monitoring data interval;
The second deletion submodule 1402, It is configured to delete the abnormal data interval.
In this embodiment, When it is judged that the abnormal data interval meets the first preset condition, Considering that the abnormal data interval is sufficient to represent the new data change trend, Other anomalous data intervals are no longer of reference value, Therefore, after the fifth determining submodule 1401 replaces the abnormal data interval with the current monitoring data interval to become a new current monitoring data interval, The second deletion submodule 1402 deletes all other abnormal data intervals.
among them, The preset frequency threshold, The preset number of thresholds, The first preset data quantity threshold, Preset times threshold, The preset time threshold and the second preset data quantity threshold can be set according to actual application needs, The present invention does not specifically limit it.
An embodiment of the present invention also discloses an electronic device, 15 is a block diagram showing the structure of an electronic device according to an embodiment of the present invention, As shown in Figure 15, The electronic device 1500 includes a memory 1501 and a processor 1502; among them,
The memory 1501 is used to store one or more computer instructions, among them, The one or more computer instructions are executed by the processor 1502 to implement any of the above method steps.
16 is a schematic structural diagram of a computer system suitable for implementing a data monitoring method according to an embodiment of the present invention.
As shown in Figure 16, The computer system 1600 includes a central processing unit (CPU) 1601, It can execute various processes in the above-described embodiments according to the program stored in the read-only memory (ROM) 1602 or the program loaded from the storage portion 1608 into the random access memory (RAM) 1603. In RAM1603, Various programs and data required for the operation of the system 1600 are also stored. CPU1601 The ROM 1602 and RAM 1603 are connected to each other through a bus 1604. An input/output (I/O) interface 1605 is also connected to the bus 1604.
The following components are connected to the I/O interface 1605: Including keyboard, Input part 1606 of a mouse, etc.; Including such as cathode ray tube (CRT), Output part 1607 of liquid crystal display (LCD) etc. and speakers etc.; Storage part 1608 including hard disk, etc.; And includes such as LAN card, The communication section 1609 of a network interface card such as a modem. The communication section 1609 performs communication processing via a network such as the Internet. The driver 1610 is also connected to the I/O interface 1605 as needed. Removable media 1611, Such as disk, CD, Magneto-optical disc, Semiconductor memory, etc., Installed on the drive 1610 as needed, The computer program read out therefrom is installed into the storage section 1608 as needed.
Particularly, According to an embodiment of the invention, The method described above can be implemented as a computer software program. E.g, Embodiments of the present invention include a computer program product, It includes computer programs tangibly contained on its readable media, The computer program includes program code for executing the data monitoring method. In such an embodiment, The computer program can be downloaded and installed from the Internet through the communication section 1609, And/or installed from the removable media 1611.
The flowchart and block diagram in the drawings, Illustrates a system according to various embodiments of the invention, Method and possible architecture of computer program products, Function and operation. At this point, Each block in the road map or block diagram can represent a module, A section of code or part of code, The module, The program segment or part of the program code contains one or more executable instructions for realizing the specified logic function. It should also be noted that In some alternative implementations, The functions marked in the blocks may also occur in an order different from that marked in the drawings. E.g, The two blocks represented in succession can actually be executed substantially in parallel, They can sometimes be executed in reverse order, It depends on the function involved. Also note that Each block in the block diagram and/or flowchart, And combinations of blocks in block diagrams and/or flowcharts, It can be implemented with a dedicated hardware-based system that performs specified functions or operations, Or it can be realized by a combination of dedicated hardware and computer instructions.
The units or modules described in the embodiments of the present invention may be implemented by software, It can also be achieved by hardware. The described unit or module may also be provided in the processor, In some cases, the names of these units or modules do not constitute restrictions on the units or modules themselves.
As another aspect, An embodiment of the present invention also provides a computer-readable storage medium, The computer-readable storage medium may be a computer-readable storage medium included in the device described in the above embodiments; Can also exist alone, Computer-readable storage media that is not installed in the device. One or more programs are stored on the computer-readable storage medium, The program is used by one or more processors to execute the method described in the embodiments of the present invention.
The above description is only the preferred embodiment of the present invention and the explanation of the applied technical principles. Those skilled in the art should understand that The scope of the invention involved in the embodiments of the present invention, It is not limited to the technical solution formed by the specific combination of the above technical features, It should also cover without departing from the inventive concept, Other technical solutions formed by any combination of the above technical features or their equivalents. For example, the above features and the technical features disclosed in the embodiments of the present invention (but not limited to) having similar functions are replaced with each other to form a technical solution.
